The New Science of Growth Marketing

Just as quantitative trading revolutionized finance in the 1980s, AI is ushering in a revolution in growth and marketing. That’s the argument from Coframe founder Josh Payne in this week’s Thesis. The new era of AI-driven growth, Payne writes, will push us forward toward a truly optimized future where businesses can better reach new audiences and turn them into happy customers.—Kate Lee

Was this newsletter forwarded to you? Sign up to get it in your inbox.

I was first exposed to the field of growth when my last company scaled from zero to unicorn status in just two years—in large part because of our team’s growth marketing efforts.

My initial impression was that growth marketers had an innate, almost mystical, sense of how to drive metrics. They were masters of finding creative ways to reach new audiences, turn those audience members into prospects, and convert those prospects into happy customers. They were “business whisperers.”

It was not unlike the impression most people have about traders who work in finance. They assume it’s a dark art of experience and instinct.

Yet even the most data-driven and logical traders still have flaws and biases, which quickly compound. In financial trading, gut instincts lead to poor decisions.

Why am I making this comparison between trading and growth? These two fields seem worlds apart. Trading is East Coast—stocks, bonds, pressed suits, and dress shoes. Growth is West Coast—viral growth loops, north star metrics, hoodies, and sneakers.

As it turns out, growth, like trading, is both an art and a science. It’s not mystical, but grounded in creating hypotheses, running experiments, and using metrics to rigorously drive success. Today, as the founder of Coframe, an AI growth copilot for websites, I know the truth: Growth people are indeed business whisperers, but their whispering is both systematic and measurable.

And now, AI is fueling a new revolution in growth and marketing: a quantitative one. Let’s look at how the quant revolution that transformed finance is doing the same to growth, the early quant experimentation strategies that are working today, and how growth and marketing teams can win by mastering it early.

But first, we need to first look at how quantitative trading ate the finance world.

How the market was solved

Two mathematicians sat facing each other amid a scattering of charts, reports, and statements. The first mathematician, Leonard Baum, reflected sullenly, “How could this have happened?” At one point, he was unstoppable. Their fund, Monemetrics, had—in the three short years since its founding in 1981—racked up over $43 million in profit. Monemetrics was on track to become one of the highest-performing hedge funds in the world.

But, as they say, in a bull market, everyone’s a genius. Following its meteoric rise, Baum’s position, built from instinctive trades and a “buy-and-hold” mentality, had gone the way of Icarus—plummeting to 40 percent in losses, triggering a clause in the fund’s operating agreement, and liquidating the entire position.

“There was no rhyme or reason,” the second mathematician thought. He was right. Jim Simons had built a career as a differential geometrist, living within a theoretical world of order, logic, and rationality. But the market is none of those things. Its movement is far too complex for any human mind to comprehend. It would not be solved on gut instinct alone.

When this painful truth sank in, Simons started over. He went on to found a new firm, Renaissance Technologies (RenTec), from the ashes of Monemetrics, and introduced what is perhaps the most important revolution in modern-day finance: quantitative trading.

Quantitative trading (or “quant trading”) hinges on a few tenets:

• Quantitative information is indicative of underlying patterns in the market.
• Computers can process and compute these patterns much more quickly and effectively than humans can.
• Algorithms can use this information to predict patterns in the market.

If you can predict patterns in the market, you’ve solved it. This early insight—using data and quantitative methods to predict patterns in the market—allowed RenTec to generate better returns than any known trading firm in history. One hundred dollars invested in RenTec’s Medallion Fund in 1988 would be worth over $400 million today.

The rest is history. Discretionary (human-driven) trading accounted for less than 10 percent of the total trading volume, as estimated by JP Morgan…back in 2017. That number is likely even lower today. Quant trading has eaten the finance world.

What Simons—and, eventually, the rest of the world—realized was that in decision-making, the way to consistently outperform everyone else is to have the best data and make the best use of it. While some traders can still outperform in longer-horizon discretionary trading, the majority of capital is best allocated to the machines.

What does all this have to do with growth?

A new era of quant experimentation

In trading, the objective is to place a series of trades to increase the value of a portfolio. This starts with market research. We use indicators—such as company fundamentals, volatility, and momentum—to deepen our understanding. As we learn about the market, we start to identify patterns and develop hypotheses about assets that the market has mispriced. But we only have so much capital, so we place our bets based on the conviction we have in our hypotheses. If we’re right, we will beat the broader market; that outperformance is called alpha. The portfolio grows, and the cycle continues.